GNNIC: Finding Long-Lost Sibling Functions with Abstract Similarity
Qiushi Wu
Network and Distributed System Security (NDSS) Symposium 2024 · Day 1 · Adversarial ML
The talk "GNNIC: Finding Long-Lost Sibling Functions with Abstract Similarity," presented by Qiushi Wu at the NDSS Symposium, addresses a pervasive and critical challenge in program analysis: the accurate construction of **call graphs** for large-scale software systems, particularly operating system kernels. Modern software extensively uses **indirect calls**, where the target function is determined at runtime via function pointers. This flexibility, while powerful, renders traditional static analysis techniques largely ineffective in precisely identifying all potential targets, leading to highly imprecise call graphs. This imprecision cascades into significant limitations for various downstream security applications, including bug detection, program debloating, directed fuzzing, and vulnerability assessment, often resulting in an overwhelming number of false positives or missed vulnerabilities.